Multi-task learning of perceptive feature for thyroid malignant probability prediction

In recent years many computer-aided diagnosis systems (CAD) using deep learning (DL) were developed for thyroid classification. However, most DL approaches have flawed clinical interpretation and often need a large amount of supervised data to ensure performance. For medical images, the costs of obtaining labeled data are relatively high, making the problem of few-shot learning (FSL) more common. We proposed a multi-task learning network for thyroid malignant probability prediction using perceptive interpretable features to overcome these limitations. With IRB approval, 1588 cases were collected with perceptive features diagnosed by experienced radiologists. The hard parameter sharing network was trained using perceptive features and pathological results as ground truth. Prior knowledge was embedded into the network by multi-task learning of perceptive features, which is how radiologists diagnose from ultrasound images. We trained the models using the 1345 cases and tested them with 243 cases. It was found that the improvement of the classifier with the proposed network (AUC of 0.879) to the baseline CNN (AUC of 0.779) was statistically significant (p <0.001).